In each of these cases, we used the four macroeconomic scenarios as a basis on which we design and elaborate further more detailed sub-scenarios of specific structural reforms.

ANNEX 3.MAIN DATA SOURCES AND DATA ASSUMPTIONS The simulation of future costs of the delivery of housing and utility services, as well as estimates of the associated demands for budgetary support were undertaken based on the following data sources and assumptions (Table A3.1).

Table A3.1: Basic Data Used in Simulations and Related Assumptions Indicator Comments 1. Average per capita household We used 2002 household income data as reported by the Roskomstat income by region. Household (2003) Regiony Rossii. Social’no-economicheskiye Pokazateli.

income distribution, by region Related assumptions: (a) For the period of forecast, the average real per capita income assumed to grow at an identical rate across regions.

(b) The income distribution by octile in each region would remain unchanged during the forecast period, i.e., the shares of households in each octile remain the same (but region specific). Average income for each octile changes is based on the assumptions about real income growth.

The forecast was based on the actual household income distribution by octile in 2002.

2. Share of population groups that Two special population groups were identified:

have much lower liabilities with (а) Families eligible for benefits on payments (lgoty) for housing and respect to housing and utility utility services, established by the federal legislation. These data were payments, by region based on the 2003 NOBUS survey.

(b) Families that occupy housing units with permanent access to running water. These data were based on Roskomstat reports for 9 months of (statistical report 22-ZhKH). The importance of separating this group relates to the fact that families without access to running water are, as a rule, occupants of sub-standard housing who do not have access to other most expensive utilities, such as district heat, sewage, and hot water. As a result, the overall costs of housing and utility services for the residents of this type of housing are considerably lower than the average. In practical terms, it means that those households in the units without running water never apply for housing allowances.

3. Monthly costs of delivering The model provides estimates for unit costs of delivering housing and housing and utility services per utility services using information on the federal standards for maximum 1 sq. m. of the occupied housing housing and utility costs per 1 sq, m. of housing in 2002, approved by the stock, by region federal government for each region (Government Decree No. 804 of November 19, 2001). The model (a) estimates the future national average costs of housing under chosen assumptions, and then (b) differentiates these costs by region, based on the historical cross-regional cost differentiation in 2002.

Related assumption: The regional proportions between housing costs will remain unchanged during the forecast period for all simulated scenarios.

4. Cross-subsidization in tariffs Estimates are based on the data on actual energy and utility tariffs by types of consumers in early 2003 as reported by the Roskomstat in Tseny i Tariffy v Zhilishno-kommunal’nom Khozyaistve 2003. Issue 1 (15).

Juanary-March.

Related assumption: Cross-subsidization in tariffs is phased out in two years (2005-06) in two equal steps.

and number of recipients), by region 6. Estimates for budget Expenditures on utility services were estimated as the share of the reported expenditures on utility services budget expenditures on particular government functions and were based provided to budget organizations upon:

• data (in economic classification) on the execution of the consolidated regional budgets in 2001 (source: www.budjetrf.ru) • data on the federal budget expenditures in 2001-03 and reports on the consolidated regional budget execution in 2002 (in functional classification) (source: www.minfin.ru) • the findings of the IUE survey for the Vologodskaya and Rostovskaya oblasts in 1999-• data on housing and utility prices and the norms of per capita consumption of these services in 2002 (source: Roskomstat Bulletin. Tseny i Tariffy v Zhilishno-kommunal’nom Khozyaistve:

1(15), 2003) • data of the Center for Facilitation of Penal Reform on the number of inmates and personnel in the penitentiary institutions as of 1 July • data on the number of military and civil personnel in the Armed Forces, the Federal Border Guard, the Interior Troops of the Ministry of Interior and on the number of staff in law enforcement agencies, the Federal Security Service and tax police (source:

www.budjetrf.ru).

Information on the incidence of housing privileges (lgoty) The information on a number of beneficiaries of lgoty by region is in principle available from three different sources:

• Roskomstat: the federal government’s statistical survey of housing and utilities (Form 26-ZKH). The latest available information is for the nine months of 2003.

• Ministry of Finance: A specially prepared data set used by the Ministry for the estimation of regional fiscal needs. The latest available information is for the first six months of 2002.

• NOBUS: National survey of household budgets and participation in social programs (Russian acronym NOBUS), undertaken for the first time in spring 2003.

Information about the number of people enjoying discounts on payments for housing and utility services has been traditionally reported by the Roskomstat in Form 26-ZhKH. A major deficiency of this source is that housing and utility providers that file Form 26-ZhKH have incentives to report larger numbers of lgoty recipients. Moreover, lack of effective control over these reports results in quite distorted statistics. In the past, despite its reliability problem, most research on the topic was based on this source just because no nationwide alternative was available.

Data from the Finance Ministry do not represent an independent source of information. They are based on Roskomstat data, but a considerable effort was made to clean up the original information and eliminate most inconsistencies. However, this data set is the most outdated. In particular, it does not reflect some reduction in a number of lgoty recipients that took place in 2002-03 owing to the monetization of their benefits.

The NOBUS survey provides the best available data to date on lgoty recipients. The survey was undertaken for the first time in April-May 2003. Forty-five thousand households in all Russian regions participated in the survey. It is believed to be much more reliable than the 26-ZhKH data, in part because the parties responsible for collecting and processing the survey returns were not interested in misreporting the results. This is the reason why this report uses the NOBUS data for the simulation of fiscal effects related to lgoty.

ANNEX 4.1.

SENSITIVITY ANALYSIS FOR PENSION SIMULATIONS Given the fact that two parameters – UST tax rate and share of payroll in GDP (i.e.

the UST tax base) — have the greatest impact on the performance of the pension system, additional sensitivity analysis of the results was undertaken to explore details of their influence on the average pension, as well as to assess potential links between such individual influences.

Figure A4.1 presents the indifference curves for the average pension in 2030, measured as its ratio to the pensioner’s subsistence minimum. These estimates correspond to the scenario 44 (advanced institutional reforms under the high oil prices). Each curve corresponds to the same ratio of average pension to subsistence minimum, i.e. it reflects the same purchasing power of the average pension. These results reflect the outcomes of about 100 simulations that correspond to specific values of these two parameters (UST rate and payroll share). In the base scenario without UST rate cut, the share of payroll is expected to increase from the current 25% to 29% of GDP, which bring the pension/subsistence ratio from 2.3 to about 2.5. The diagram also shows how much the payroll share should increase to keep the ratio roughly at the same level of 2.5 under different assumptions regarding the magnitude of the tax cut. If the rate is cut by 4 p.p., the share has to reach 36% to keep the pension/subsistence ratio intact.

In general, 1 p.p. decline in the UST rate could be compensated by an increase in the payroll share by about 1.5 p.p. At every specific tax rate the decline in the payroll share by p.p. brings the pension/subsistence ratio down by about 12 points, from 2.5 to 2.38. The latter result suggests that our estimates are rather robust: substantial fluctuations in the payroll share cause modest changes in the purchasing power of the average pension.

Figure A4.2 presents a more accurate non-linear approximation for the relationship between these two parameters that corresponds to the indifference curve of 2.3 at Figure A4.1.

Figure A4.3 presents a similar set of indifference curves for the replacement rate.

The presented approach helps expand understanding of potential dynamics of the analyzed variables. Instead of generating specific point estimates of particular parameters, it focuses on larger intervals of policy variables, within which the performance characteristics of the pension system remain sensible. It also helps to concentrate the analysis on the issue of internal consistency of the assumptions: how realistic is that the future joint dynamic of main parameters would go in a way that would ensure a stable performance of the system Figure A4.1: Indifference curves for the pension system: the ratio between the average pension and pensioner’s subsistence, Share of payroll in GDP, % Additional growth in payroll, triggered by the cuts in UST rates 28 Growth in the payroll w/o cuts in UST rates 5 6 7 8 10 11 12 13 UST rate (base pension) Figure A4.2: Indifference curve for the pension system: compensatory relationship between an increase in the payroll share and cuts in the UST rate.

Payroll share in GDP, % y = 0,09(x+5) - 2,21(x+5) + 34, -4 -3 -2 -1 Change in the UST rate (base pension) Figure A4.3: Indifference curve for the pension system: replacement ratio in the PAYG system, Replacement Rate Payroll share in GDP 5 6 7 8 9 10 11 12 13 UST Rate ANNEX 4.SIMULATION METHODOLOGY Simulation methodology used in this report is close to the approach employed in the PROST program by the World Bank. Accordingly, the type of model used in this study had been employed frequently by the World Bank for undertaking similar analyses in other countries. It is also worth noting that the model followed some implementation principles that are similar to those used in the ILO Social Budget Program. The latter related e.g., to the model implementation in Excel with partial use of the Visual Basic. On the one hand, such an approach allows for more transparency in calculations because intermediate results for each program block are stored in separate spreadsheets of Excel books making it easier to trace links between the spreadsheets and check the formulas. On the other hand, rather complicated links between the spreadsheets present certain difficulties for the analysis of calculation sequences, and the formulas inserted directly in EXCEL cells make the model more vulnerable because it is easier to make a mistake in copying formulas, while it is rather time consuming to change separate formulas.

A brief description of the model is provided in the Annex to the World Bank (2003с) report, as well as in Simulation and Actuarial Estimates (2003). The earlier version of the model was reviewed by the TACIS consultants, and their main comments (Stott, 2002), such as more accurate reflection of pension contributions by working pensioners, were reflected in the current version of the model.

The simulation program used for analysis in Chapter 4 was fully implemented in Visual Basic. The same simulation technique was previously used for the preparation of the World Bank Report (2003). A specific structural feature of this version of the computer program as compared to the earlier version described in Baskakov et al. (2003) is its analytical focus, absence of interface forms and reliance on bar chart reports, which substantially facilitate the analysis and quality control of results obtained.

As compared with the World Bank (2003с) report, the simulations in Chapter 4 are based on a radically revised set of assumptions regarding Russia’s macroeconomic projections that reflect the actual changes in the country’s economic performance that occurred in 2001-03. Besides, we consider a broader range of potential development scenarios.

In addition, it is worth mentioning a certain difference in the logic between this model and the model in the World Bank (2003с) report. The latter modeled the accelerated growth of the payroll based on the explicit assumption about difference in growth rates between average wage and productivity. In this paper, the trend in the payroll share in GDP is instead considered as a key exogenous variable. This trend is described in the model by a conventional exponential transition process. Such an approach adds flexibility to the model.

In particular, it allows undertaking additional analysis of the pension system’s sensitivity to changes in the basic trends in taxable wages.

The following set of basic input variables in the model determines most of the variability within and across the scenario groups: